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Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey

Mehdi Gheisari Orcid Logo, Fereshteh Ebrahimzadeh, Mohamadtaghi Rahimi, Mahdieh Moazzamigodarzi, Yang Liu Orcid Logo, Pijush Kanti Dutta Pramanik, Mohammad Ali Heravi, Abolfazl Mehbodniya Orcid Logo, Mustafa Ghaderzadeh Orcid Logo, Mohammad Reza Feylizadeh, Saeed Kosari

CAAI Transactions on Intelligence Technology, Volume: 8, Issue: 3, Pages: 581 - 606

Swansea University Author: Yang Liu Orcid Logo

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DOI (Published version): 10.1049/cit2.12180

Abstract

Deep Learning (DL) is a subfield of machine learning that significantly impacts extracting new knowledge. By using DL, the extraction of advanced data representations and knowledge can be made possible. Highly effective DL techniques help to find more hidden knowledge. Deep learning has a promising...

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Published in: CAAI Transactions on Intelligence Technology
ISSN: 2468-2322 2468-2322
Published: Institution of Engineering and Technology (IET) 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa67387
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last_indexed 2024-11-25T14:20:05Z
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spelling 2024-09-20T15:41:00.2837466 v2 67387 2024-08-15 Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey ba37dab58c9093dc63c79001565b75d4 0000-0003-2486-5765 Yang Liu Yang Liu true false 2024-08-15 MACS Deep Learning (DL) is a subfield of machine learning that significantly impacts extracting new knowledge. By using DL, the extraction of advanced data representations and knowledge can be made possible. Highly effective DL techniques help to find more hidden knowledge. Deep learning has a promising future due to its great performance and accuracy. We need to understand the fundamentals and the state-of-the-art of DL to leverage it effectively. A survey on DL ways, advantages, drawbacks, architectures, and methods to have a straightforward and clear understanding of it from different views is explained in the paper. Moreover, the existing related methods are compared with each other, and the application of DL is described in some applications, such as medical image analysis, handwriting recognition, and so on. Journal Article CAAI Transactions on Intelligence Technology 8 3 581 606 Institution of Engineering and Technology (IET) 2468-2322 2468-2322 data mining; data privacy; deep learning 1 9 2023 2023-09-01 10.1049/cit2.12180 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University 2024-09-20T15:41:00.2837466 2024-08-15T16:58:09.2191121 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Mehdi Gheisari 0000-0002-5643-0021 1 Fereshteh Ebrahimzadeh 2 Mohamadtaghi Rahimi 3 Mahdieh Moazzamigodarzi 4 Yang Liu 0000-0003-2486-5765 5 Pijush Kanti Dutta Pramanik 6 Mohammad Ali Heravi 7 Abolfazl Mehbodniya 0000-0002-0945-512x 8 Mustafa Ghaderzadeh 0000-0003-4016-3843 9 Mohammad Reza Feylizadeh 10 Saeed Kosari 11 67387__31421__236565c5fe1442b2b1d24bb9d3ea9bbd.pdf 67387.VoR.pdf 2024-09-20T15:39:04.3774573 Output 5594623 application/pdf Version of Record true © 2023 The Authors. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License. true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey
spellingShingle Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey
Yang Liu
title_short Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey
title_full Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey
title_fullStr Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey
title_full_unstemmed Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey
title_sort Deep learning: Applications, architectures, models, tools, and frameworks: A comprehensive survey
author_id_str_mv ba37dab58c9093dc63c79001565b75d4
author_id_fullname_str_mv ba37dab58c9093dc63c79001565b75d4_***_Yang Liu
author Yang Liu
author2 Mehdi Gheisari
Fereshteh Ebrahimzadeh
Mohamadtaghi Rahimi
Mahdieh Moazzamigodarzi
Yang Liu
Pijush Kanti Dutta Pramanik
Mohammad Ali Heravi
Abolfazl Mehbodniya
Mustafa Ghaderzadeh
Mohammad Reza Feylizadeh
Saeed Kosari
format Journal article
container_title CAAI Transactions on Intelligence Technology
container_volume 8
container_issue 3
container_start_page 581
publishDate 2023
institution Swansea University
issn 2468-2322
2468-2322
doi_str_mv 10.1049/cit2.12180
publisher Institution of Engineering and Technology (IET)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
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description Deep Learning (DL) is a subfield of machine learning that significantly impacts extracting new knowledge. By using DL, the extraction of advanced data representations and knowledge can be made possible. Highly effective DL techniques help to find more hidden knowledge. Deep learning has a promising future due to its great performance and accuracy. We need to understand the fundamentals and the state-of-the-art of DL to leverage it effectively. A survey on DL ways, advantages, drawbacks, architectures, and methods to have a straightforward and clear understanding of it from different views is explained in the paper. Moreover, the existing related methods are compared with each other, and the application of DL is described in some applications, such as medical image analysis, handwriting recognition, and so on.
published_date 2023-09-01T08:17:37Z
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score 11.059058

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